Just got invited to the QuantumBlack (McKinsey) Data Scientist Intern Online Assessment – what do I actually need to know to not blow this?
I'm a business/quant student in continental Europe, finishing my bachelor's and heading into an MSc in Financial Engineering next year. Applied on a bit of a long shot — I'm not a pure CS or stats major — and somehow got the HackerRank invite for the Data Scientist Intern position.
I genuinely feel like if I can land this, it sets the trajectory for the rest of my early career. I'm willing to clear my schedule and go all-in on prep for every round.
From the prep booklet they sent, it looks like the process is:
**Pre-stage:** HackerRank (modelling + technical knowledge questions)
**Round 1:** Experience Interview + Business Case, AND a Technical Experience Interview + Practical Skills Assessment (pair programming in Python on HackerRank)
**Round 2:** Another Experience Interview + Business Case
Right now I'm focused on Round 0 – the HackerRank OA. For those who've been through it or something similar:
**1. What specific tips do you have for the OA?**
- How long was it really? The booklet says anywhere from 10 to 90 min.
- Was it more coding/modelling or more MCQ-style technical knowledge?
- Did you get a mix of both or just one type?
- Any gotchas or things that caught you off guard?
**2. What should I be preparing?**
- The job description mentions pandas, NumPy, scikit-learn, EDA, RAG/LLM use cases, and statistical analysis. Is that actually what shows up on the test?
- Should I focus more on ML fundamentals (bias-variance, cross-validation, metrics) or on actual coding tasks (data wrangling, feature engineering, building a baseline model)?
- Any SQL or stats theory questions?
**3. Best study materials / resources?**
- Is StrataScratch or LeetCode more relevant for this type of DS assessment?
- Any specific Kaggle notebooks or courses that mirror the format?
- Textbook or cheat sheet recommendations for brushing up on sklearn workflows, model evaluation, probability/stats?
**4. Things I might be forgetting?**
- Did anyone get questions on experiment design (A/B testing, hypothesis testing)?
- Data visualization or interpretation questions?
- Anything on GenAI / RAG / LLM concepts given that's in the job description?
- Time series, NLP, or deep learning – or is it strictly classical ML + stats?
- How important is writing clean, well-structured code vs. just getting the right answer?
For context: I have working knowledge of Python (pandas, sklearn, numpy), R, SQL, and some experience with API-based data pipelines and quant simulations. My programme has solid stats and econometrics foundations, but my DSA fundamentals are decent, not competition-level.
Any advice – even just "focus on X, ignore Y" – would be massively appreciated. Happy to pay it forward and post my experience after the process.
Thanks in advance 🙏